Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Grasp as You Say: Language-guided Dexterous Grasp Generation
Authors: Yi-Lin Wei, Jian-Jian Jiang, Chengyi Xing, Xian-Tuo Tan, Xiao-Ming Wu, Hao Li, Mark Cutkosky, Wei-Shi Zheng
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on Dex GYSNet and real world environments for validation. |
| Researcher Affiliation | Academia | 1 School of Computer Science and Engineering, Sun Yat-sen University, China 2 Stanford University, USA 3 Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China |
| Pseudocode | No | The paper describes methods and frameworks in text and diagrams but does not include explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | We promise to release all code and the complete dataset after the publication of this paper. |
| Open Datasets | Yes | We first collect object meshes and human grasps data from existing datasets [27]. |
| Dataset Splits | Yes | We split the Dex DYSNet dataset at the object instance level, using 80% of the objects within each category for training and 20% for evaluation. |
| Hardware Specification | Yes | All experiment are implemented with Py Torch on a single RTX 4090 GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch' as the implementation framework but does not provide specific version numbers for it or any other software dependencies. |
| Experiment Setup | Yes | For training our framework, the training epochs are set to 100 for intention and diversity grasp component and 20 for Quality Grasp Component. The loss weights are configured as follows: λ2 para = λ3 para = 10, λ2 chamfer = λ3 chamfer = 1, λ3 cmap = 10, λ3 pen = 100, λ3 spen = 10. Throughout all training processes, the model is optimized with a batch size of 64 using the Adam optimizer, with a weight decay rate of 5.0 10 6. The initial learning rate is 2.0 10 4 and decay to 2.0 10 5 using a cosine learning rate [52] scheduler. |